Prioritizing Strategies of Relationship with Retail Customers in Several Levels of Life Cycle by Integrated Approach: KANO-IPA-QFD-TOPSIS
Bibliographic record
Abstract
Today, customer orientation and relationship with customers is considered as one of the main strategies of development in organizations. In this regard, it is necessary to create a process that is able of making relationship with different costumers due to their needs and demands. Quality function development (QFD) is one of the common methods regarding to attention to customers’ needs and adopting them with definitions and features of costumers in several life cycle levels. In this article, first technical features ( relationship strategies) and relationship needs of retail customers (features of relationship strategies) are recognized; then, strategies of costumers relationship ion each level of life cycle are prioritized using integrated technique of quality QFD and TOPSIS. The results show prioritization of new relationship strategies, especially internet communications compared to other strategies. This study is considered as an efficient step to improve relationship with costumers and as a result, to keep and develop organizations’ development status using information gathered from costumers in different levels of life cycle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".